Machine Learning

Machine Learning is used to address critical national and global issues by applying scientific and mathematical techniques in artificial intelligence to multiple data sources and communicating these findings to the community. Researchers also develop novel, interpretable, and scalable machine learning algorithms and applications, designed to run on systems as large as current DOE leadership class facilities all the way to commodity hardware.

Significant Projects

Deep Learning

Contemporary deep learning has enabled the next generation of artificial intelligence applications, opening the door for major breakthroughs. PNNL applies deep learning across its mission sciences in energy, biology, the environment, and national security to better leverage data and computational resources to accelerate innovation and enhance scientific discovery.

Power Systems

This project investigates an ensemble-based technique, Bayesian Model Averaging (BMA), to improve Net Interchange Schedule (NIS) forecast performance. This work illustrates a possible new mechanism for improving NIS forecasting accuracy, as well as other power grid system variables.

This project is exploring the sensitivity of high-performance computing applications to faults and approximation, understanding performance and accuracy trade-offs, and building models to determine if an application is suitable for approximation.